`ultralytics 8.0.107` (#2778)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Peter van Lunteren <contact@pvanlunteren.com>

README.md

@@ -30,7 +30,7 @@ To request an Enterprise License please complete the form at [Ultralytics Licens
   <a href="https://github.com/ultralytics" style="text-decoration:none;">
     <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="2%" alt="" /></a>
   <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
-  <a href="https://www.linkedin.com/company/ultralytics" style="text-decoration:none;">
+  <a href="https://www.linkedin.com/company/ultralytics/" style="text-decoration:none;">
     <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="2%" alt="" /></a>
   <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
   <a href="https://twitter.com/ultralytics" style="text-decoration:none;">
@@ -242,7 +242,7 @@ For YOLOv8 bug reports and feature requests please visit [GitHub Issues](https:/
   <a href="https://github.com/ultralytics" style="text-decoration:none;">
     <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="3%" alt="" /></a>
   <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="" />
-  <a href="https://www.linkedin.com/company/ultralytics" style="text-decoration:none;">
+  <a href="https://www.linkedin.com/company/ultralytics/" style="text-decoration:none;">
     <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="3%" alt="" /></a>
   <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="" />
   <a href="https://twitter.com/ultralytics" style="text-decoration:none;">

README.zh-CN.md

@@ -30,7 +30,7 @@
   <a href="https://github.com/ultralytics" style="text-decoration:none;">
     <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="2%" alt="" /></a>
   <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
-  <a href="https://www.linkedin.com/company/ultralytics" style="text-decoration:none;">
+  <a href="https://www.linkedin.com/company/ultralytics/" style="text-decoration:none;">
     <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="2%" alt="" /></a>
   <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
   <a href="https://twitter.com/ultralytics" style="text-decoration:none;">
@@ -241,7 +241,7 @@ YOLOv8 提供两种不同的许可证：
   <a href="https://github.com/ultralytics" style="text-decoration:none;">
     <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="3%" alt="" /></a>
   <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="" />
-  <a href="https://www.linkedin.com/company/ultralytics" style="text-decoration:none;">
+  <a href="https://www.linkedin.com/company/ultralytics/" style="text-decoration:none;">
     <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="3%" alt="" /></a>
   <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="" />
   <a href="https://twitter.com/ultralytics" style="text-decoration:none;">

docs/datasets/classify/caltech101.md

@@ -66,9 +66,7 @@ If you use the Caltech-101 dataset in your research or development work, please
   author={Fei-Fei, Li and Fergus, Rob and Perona, Pietro},
   journal={Computer vision and Image understanding},
   volume={106},
-  number={
-
-1},
+  number={1},
   pages={59--70},
   year={2007},
   publisher={Elsevier}

docs/datasets/classify/caltech256.md

@@ -62,9 +62,9 @@ If you use the Caltech-256 dataset in your research or development work, please
 
 ```bibtex
 @article{griffin2007caltech,
-  title={Caltech-256 object category dataset},
-  author={Griffin, Gregory and Holub, Alex and Perona, Pietro},
-  year={2007}
+         title={Caltech-256 object category dataset},
+         author={Griffin, Gregory and Holub, Alex and Perona, Pietro},
+         year={2007}
 }
 ```
 

docs/datasets/classify/cifar10.md

@@ -65,10 +65,10 @@ If you use the CIFAR-10 dataset in your research or development work, please cit
 
 ```bibtex
 @TECHREPORT{Krizhevsky09learningmultiple,
-    author = {Alex Krizhevsky},
-    title = {Learning multiple layers of features from tiny images},
-    institution = {},
-    year = {2009}
+            author={Alex Krizhevsky},
+            title={Learning multiple layers of features from tiny images},
+            institution={},
+            year={2009}
 }
 ```
 

docs/datasets/classify/cifar100.md

@@ -65,10 +65,10 @@ If you use the CIFAR-100 dataset in your research or development work, please ci
 
 ```bibtex
 @TECHREPORT{Krizhevsky09learningmultiple,
-    author = {Alex Krizhevsky},
-    title = {Learning multiple layers of features from tiny images},
-    institution = {},
-    year = {2009}
+            author={Alex Krizhevsky},
+            title={Learning multiple layers of features from tiny images},
+            institution={},
+            year={2009}
 }
 ```
 

docs/datasets/classify/imagenet.md

@@ -65,15 +65,13 @@ If you use the ImageNet dataset in your research or development work, please cit
 
 ```bibtex
 @article{ILSVRC15,
-  Author = {Olga Russakovsky
-
- and Jia Deng and Hao Su and Jonathan Krause and Sanjeev Satheesh and Sean Ma and Zhiheng Huang and Andrej Karpathy and Aditya Khosla and Michael Bernstein and Alexander C. Berg and Li Fei-Fei},
-  Title = { {ImageNet Large Scale Visual Recognition Challenge}},
-  Year = {2015},
-  journal   = {International Journal of Computer Vision (IJCV)},
-  volume={115},
-  number={3},
-  pages={211-252}
+         author = {Olga Russakovsky and Jia Deng and Hao Su and Jonathan Krause and Sanjeev Satheesh and Sean Ma and Zhiheng Huang and Andrej Karpathy and Aditya Khosla and Michael Bernstein and Alexander C. Berg and Li Fei-Fei},
+         title={ImageNet Large Scale Visual Recognition Challenge},
+         year={2015},
+         journal={International Journal of Computer Vision (IJCV)},
+         volume={115},
+         number={3},
+         pages={211-252}
 }
 ```
 

docs/datasets/classify/imagenet10.md

@@ -60,17 +60,13 @@ If you use the ImageNet10 dataset in your research or development work, please c
 
 ```bibtex
 @article{ILSVRC15,
-  Author = {Olga Russakovsky
-
- and Jia Deng and Hao Su and Jonathan Krause and Sanjeev Satheesh and Sean Ma and Zhiheng Huang and Andrej Karpathy and Aditya Khosla and Michael Bernstein and Alexander C. Berg and Li Fei-Fei},
-  Title = { {ImageNet Large
-
-Scale Visual Recognition Challenge}},
-  Year = {2015},
-  journal   = {International Journal of Computer Vision (IJCV)},
-  volume={115},
-  number={3},
-  pages={211-252}
+         author = {Olga Russakovsky and Jia Deng and Hao Su and Jonathan Krause and Sanjeev Satheesh and Sean Ma and Zhiheng Huang and Andrej Karpathy and Aditya Khosla and Michael Bernstein and Alexander C. Berg and Li Fei-Fei},
+         title={ImageNet Large Scale Visual Recognition Challenge},
+         year={2015},
+         journal={International Journal of Computer Vision (IJCV)},
+         volume={115},
+         number={3},
+         pages={211-252}
 }
 ```
 

docs/datasets/classify/mnist.md

@@ -70,11 +70,11 @@ research or development work, please cite the following paper:
 
 ```bibtex
 @article{lecun2010mnist,
-  title={MNIST handwritten digit database},
-  author={LeCun, Yann and Cortes, Corinna and Burges, CJ},
-  journal={ATT Labs [Online]. Available: http://yann.lecun.com/exdb/mnist},
-  volume={2},
-  year={2010}
+         title={MNIST handwritten digit database},
+         author={LeCun, Yann and Cortes, Corinna and Burges, CJ},
+         journal={ATT Labs [Online]. Available: http://yann.lecun.com/exdb/mnist},
+         volume={2},
+         year={2010}
 }
 ```
 

docs/datasets/detect/globalwheat2020.md

@@ -76,10 +76,10 @@ If you use the Global Wheat Head Dataset in your research or development work, p
 
 ```bibtex
 @article{david2020global,
-  title={Global Wheat Head Detection (GWHD) Dataset: A Large and Diverse Dataset of High-Resolution RGB-Labelled Images to Develop and Benchmark Wheat Head Detection Methods},
-  author={David, Etienne and Madec, Simon and Sadeghi-Tehran, Pouria and Aasen, Helge and Zheng, Bangyou and Liu, Shouyang and Kirchgessner, Norbert and Ishikawa, Goro and Nagasawa, Koichi and Badhon, Minhajul and others},
-  journal={arXiv preprint arXiv:2005.02162},
-  year={2020}
+         title={Global Wheat Head Detection (GWHD) Dataset: A Large and Diverse Dataset of High-Resolution RGB-Labelled Images to Develop and Benchmark Wheat Head Detection Methods},
+         author={David, Etienne and Madec, Simon and Sadeghi-Tehran, Pouria and Aasen, Helge and Zheng, Bangyou and Liu, Shouyang and Kirchgessner, Norbert and Ishikawa, Goro and Nagasawa, Koichi and Badhon, Minhajul and others},
+         journal={arXiv preprint arXiv:2005.02162},
+         year={2020}
 }
 ```
 

docs/hub/app/index.md

@@ -12,7 +12,7 @@ description: Experience the power of YOLOv5 and YOLOv8 models with Ultralytics H
   <a href="https://github.com/ultralytics" style="text-decoration:none;">
     <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="2%" alt="" /></a>
   <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
-  <a href="https://www.linkedin.com/company/ultralytics" style="text-decoration:none;">
+  <a href="https://www.linkedin.com/company/ultralytics/" style="text-decoration:none;">
     <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="2%" alt="" /></a>
   <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
   <a href="https://twitter.com/ultralytics" style="text-decoration:none;">

docs/yolov5/index.md

@@ -75,7 +75,7 @@ hours and on every commit.
   <a href="https://github.com/ultralytics" style="text-decoration:none;">
     <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="3%" alt="" /></a>
   <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="" />
-  <a href="https://www.linkedin.com/company/ultralytics" style="text-decoration:none;">
+  <a href="https://www.linkedin.com/company/ultralytics/" style="text-decoration:none;">
     <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="3%" alt="" /></a>
   <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="" />
   <a href="https://twitter.com/ultralytics" style="text-decoration:none;">

mkdocs.yml

@@ -400,6 +400,7 @@ plugins:
         yolov5/pytorch_hub.md: yolov5/tutorials/pytorch_hub_model_loading.md
         yolov5/hyp_evolution.md: yolov5/tutorials/hyperparameter_evolution.md
         yolov5/pruning_sparsity.md: yolov5/tutorials/model_pruning_and_sparsity.md
+        yolov5/roboflow.md: yolov5/tutorials/roboflow_datasets_integration.md
         yolov5/comet.md: yolov5/tutorials/comet_logging_integration.md
         yolov5/clearml.md: yolov5/tutorials/clearml_logging_integration.md
         yolov5/tta.md: yolov5/tutorials/test_time_augmentation.md

ultralytics/__init__.py

@@ -1,6 +1,6 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
-__version__ = '8.0.106'
+__version__ = '8.0.107'
 
 from ultralytics.hub import start
 from ultralytics.vit.rtdetr import RTDETR

ultralytics/yolo/data/augment.py

@@ -766,9 +766,17 @@ def v8_transforms(dataset, imgsz, hyp):
             pre_transform=LetterBox(new_shape=(imgsz, imgsz)),
         )])
     flip_idx = dataset.data.get('flip_idx', None)  # for keypoints augmentation
-    if dataset.use_keypoints and flip_idx is None and hyp.fliplr > 0.0:
-        hyp.fliplr = 0.0
-        LOGGER.warning("WARNING ⚠️ No `flip_idx` provided while training keypoints, setting augmentation 'fliplr=0.0'")
+    if dataset.use_keypoints:
+        kpt_shape = dataset.data.get('kpt_shape', None)
+        if flip_idx is None and hyp.fliplr > 0.0:
+            hyp.fliplr = 0.0
+            LOGGER.warning("WARNING ⚠️ No 'flip_idx' array defined in data.yaml, setting augmentation 'fliplr=0.0'")
+        elif flip_idx:
+            if len(flip_idx) != kpt_shape[0]:
+                raise ValueError(f'data.yaml flip_idx={flip_idx} length must be equal to kpt_shape[0]={kpt_shape[0]}')
+            elif flip_idx[0] != 0:
+                raise ValueError(f'data.yaml flip_idx={flip_idx} must be zero-index (start from 0)')
+
     return Compose([
         pre_transform,
         MixUp(dataset, pre_transform=pre_transform, p=hyp.mixup),

ultralytics/yolo/data/utils.py

@@ -266,7 +266,7 @@ def check_det_dataset(dataset, autodownload=True):
     return data  # dictionary
 
 
-def check_cls_dataset(dataset: str):
+def check_cls_dataset(dataset: str, split=''):
     """
     Check a classification dataset such as Imagenet.
 
@@ -275,6 +275,7 @@ def check_cls_dataset(dataset: str):
 
     Args:
         dataset (str): Name of the dataset.
+        split (str, optional): Dataset split, either 'val', 'test', or ''. Defaults to ''.
 
     Returns:
         data (dict): A dictionary containing the following keys and values:
@@ -298,10 +299,15 @@ def check_cls_dataset(dataset: str):
     train_set = data_dir / 'train'
     val_set = data_dir / 'val' if (data_dir / 'val').exists() else None  # data/test or data/val
     test_set = data_dir / 'test' if (data_dir / 'test').exists() else None  # data/val or data/test
+    if split == 'val' and not val_set:
+        LOGGER.info("WARNING ⚠️ Dataset 'split=val' not found, using 'split=test' instead.")
+    elif split == 'test' and not test_set:
+        LOGGER.info("WARNING ⚠️ Dataset 'split=test' not found, using 'split=val' instead.")
+
     nc = len([x for x in (data_dir / 'train').glob('*') if x.is_dir()])  # number of classes
     names = [x.name for x in (data_dir / 'train').iterdir() if x.is_dir()]  # class names list
     names = dict(enumerate(sorted(names)))
-    return {'train': train_set, 'val': val_set, 'test': test_set, 'nc': nc, 'names': names}
+    return {'train': train_set, 'val': val_set or test_set, 'test': test_set or val_set, 'nc': nc, 'names': names}
 
 
 class HUBDatasetStats():

ultralytics/yolo/engine/validator.py

@@ -126,7 +126,7 @@ class BaseValidator:
             if isinstance(self.args.data, str) and self.args.data.endswith('.yaml'):
                 self.data = check_det_dataset(self.args.data)
             elif self.args.task == 'classify':
-                self.data = check_cls_dataset(self.args.data)
+                self.data = check_cls_dataset(self.args.data, split=self.args.split)
             else:
                 raise FileNotFoundError(emojis(f"Dataset '{self.args.data}' for task={self.args.task} not found ❌"))
 

ultralytics/yolo/utils/benchmarks.py

@@ -3,7 +3,8 @@
 Benchmark a YOLO model formats for speed and accuracy
 
 Usage:
-    from ultralytics.yolo.utils.benchmarks import run_benchmarks
+    from ultralytics.yolo.utils.benchmarks import ProfileModels, run_benchmarks
+    ProfileModels(['yolov8n.yaml', 'yolov8s.yaml'])
     run_benchmarks(model='yolov8n.pt', imgsz=160)
 
 Format                  | `format=argument`         | Model
@@ -22,14 +23,19 @@ TensorFlow.js           | `tfjs`                    | yolov8n_web_model/
 PaddlePaddle            | `paddle`                  | yolov8n_paddle_model/
 """
 
+import glob
 import platform
 import time
 from pathlib import Path
 
+import numpy as np
+import torch.cuda
+from tqdm import tqdm
+
 from ultralytics import YOLO
 from ultralytics.yolo.engine.exporter import export_formats
 from ultralytics.yolo.utils import LINUX, LOGGER, MACOS, ROOT, SETTINGS
-from ultralytics.yolo.utils.checks import check_yolo
+from ultralytics.yolo.utils.checks import check_requirements, check_yolo
 from ultralytics.yolo.utils.downloads import download
 from ultralytics.yolo.utils.files import file_size
 from ultralytics.yolo.utils.torch_utils import select_device
@@ -140,5 +146,140 @@ def benchmark(model=Path(SETTINGS['weights_dir']) / 'yolov8n.pt',
     return df
 
 
+class ProfileModels:
+    """
+    ProfileModels class for profiling different models on ONNX and TensorRT.
+
+    This class profiles the performance of different models, provided their paths. The profiling includes parameters such as
+    model speed and FLOPs.
+
+    Attributes:
+        paths (list): Paths of the models to profile.
+        num_timed_runs (int): Number of timed runs for the profiling. Default is 100.
+        num_warmup_runs (int): Number of warmup runs before profiling. Default is 3.
+        imgsz (int): Image size used in the models. Default is 640.
+
+    Methods:
+        profile(): Profiles the models and prints the result.
+    """
+
+    def __init__(self, paths: list, num_timed_runs=100, num_warmup_runs=3, imgsz=640, trt=True):
+        self.paths = paths
+        self.num_timed_runs = num_timed_runs
+        self.num_warmup_runs = num_warmup_runs
+        self.imgsz = imgsz
+        self.trt = trt  # run TensorRT profiling
+        self.profile()  # run profiling
+
+    def profile(self):
+        files = self.get_files()
+
+        if not files:
+            print('No matching *.pt or *.onnx files found.')
+            return
+
+        table_rows = []
+        device = 0 if torch.cuda.is_available() else 'cpu'
+        for file in files:
+            engine_file = ''
+            if file.suffix in ('.pt', '.yaml'):
+                model = YOLO(str(file))
+                num_params, num_flops = model.info()
+                if self.trt and device == 0:
+                    engine_file = model.export(format='engine', half=True, imgsz=self.imgsz, device=device)
+                onnx_file = model.export(format='onnx', half=True, imgsz=self.imgsz, simplify=True, device=device)
+            elif file.suffix == '.onnx':
+                num_params, num_flops = self.get_onnx_model_info(file)
+                onnx_file = file
+            else:
+                continue
+
+            t_engine = self.profile_tensorrt_model(str(engine_file))
+            t_onnx = self.profile_onnx_model(str(onnx_file))
+            table_rows.append(self.generate_table_row(file.stem, t_onnx, t_engine, num_params, num_flops))
+
+        self.print_table(table_rows)
+
+    def get_files(self):
+        files = []
+        for path in self.paths:
+            path = Path(path)
+            if path.is_dir():
+                extensions = ['*.pt', '*.onnx', '*.yaml']
+                files.extend([file for ext in extensions for file in glob.glob(str(path / ext))])
+            elif path.suffix in {'.pt', '.yaml'}:  # add non-existing
+                files.append(str(path))
+            else:
+                files.extend(glob.glob(str(path)))
+
+        print(f'Profiling: {sorted(files)}')
+        return [Path(file) for file in sorted(files)]
+
+    def get_onnx_model_info(self, onnx_file: str):
+        return 0.0, 0.0
+
+    def profile_tensorrt_model(self, engine_file: str):
+        if not Path(engine_file).is_file():
+            return 0.0, 0.0
+
+        # Warmup runs
+        model = YOLO(engine_file)
+        input_data = np.random.rand(self.imgsz, self.imgsz, 3).astype(np.float32)
+        for _ in range(self.num_warmup_runs):
+            model(input_data, verbose=False)
+
+        # Timed runs
+        run_times = []
+        for _ in tqdm(range(self.num_timed_runs), desc=engine_file):
+            results = model(input_data, verbose=False)
+            run_times.append(results[0].speed['inference'])  # Convert to milliseconds
+
+        return np.mean(run_times), np.std(run_times)
+
+    def profile_onnx_model(self, onnx_file: str):
+        check_requirements('onnxruntime')
+        import onnxruntime as ort
+
+        # Session with either 'TensorrtExecutionProvider', 'CUDAExecutionProvider', 'CPUExecutionProvider'
+        sess_options = ort.SessionOptions()
+        sess_options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL
+        sess = ort.InferenceSession(onnx_file, sess_options, providers=['CPUExecutionProvider'])
+
+        input_tensor = sess.get_inputs()[0]
+        input_data = np.random.rand(*input_tensor.shape).astype(np.float16 if torch.cuda.is_available() else np.float32)
+        input_name = input_tensor.name
+        output_name = sess.get_outputs()[0].name
+
+        # Warmup runs
+        for _ in range(self.num_warmup_runs):
+            sess.run([output_name], {input_name: input_data})
+
+        # Timed runs
+        run_times = []
+        for _ in tqdm(range(self.num_timed_runs), desc=onnx_file):
+            start_time = time.time()
+            sess.run([output_name], {input_name: input_data})
+            run_times.append((time.time() - start_time) * 1000)  # Convert to milliseconds
+
+        return np.mean(run_times), np.std(run_times)
+
+    def generate_table_row(self, model_name, t_onnx, t_engine, num_params, num_flops):
+        return f'| {model_name} | {self.imgsz} | - | {t_onnx[0]:.2f} ± {t_onnx[1]:.2f} ms | {t_engine[0]:.2f} ± {t_engine[1]:.2f} ms | {num_params / 1e6:.1f} | {num_flops:.1f} |'
+
+    def print_table(self, table_rows):
+        gpu = torch.cuda.get_device_name(0) if torch.cuda.is_available() else 'GPU'
+        header = f'| Model | size<br><sup>(pixels) | mAP<sup>val<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>{gpu} TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |'
+        separator = '|-------------|---------------------|--------------------|------------------------------|-----------------------------------|------------------|-----------------|'
+
+        print(header)
+        print(separator)
+        for row in table_rows:
+            print(row)
+
+
 if __name__ == '__main__':
+    # Benchmark all export formats
     benchmark()
+
+    # Profiling models on ONNX and TensorRT
+    ProfileModels(['yolov8n.yaml', 'yolov8s.yaml'])

ultralytics/yolo/utils/metrics.py

@@ -67,7 +67,7 @@ def box_iou(box1, box2, eps=1e-7):
 
     # inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2)
     (a1, a2), (b1, b2) = box1.unsqueeze(1).chunk(2, 2), box2.unsqueeze(0).chunk(2, 2)
-    inter = (torch.min(a2, b2) - torch.max(a1, b1)).clamp(0).prod(2)
+    inter = (torch.min(a2, b2) - torch.max(a1, b1)).clamp_(0).prod(2)
 
     # IoU = inter / (area1 + area2 - inter)
     return inter / ((a2 - a1).prod(2) + (b2 - b1).prod(2) - inter + eps)
@@ -104,8 +104,8 @@ def bbox_iou(box1, box2, xywh=True, GIoU=False, DIoU=False, CIoU=False, eps=1e-7
         w2, h2 = b2_x2 - b2_x1, b2_y2 - b2_y1 + eps
 
     # Intersection area
-    inter = (b1_x2.minimum(b2_x2) - b1_x1.maximum(b2_x1)).clamp(0) * \
-            (b1_y2.minimum(b2_y2) - b1_y1.maximum(b2_y1)).clamp(0)
+    inter = (b1_x2.minimum(b2_x2) - b1_x1.maximum(b2_x1)).clamp_(0) * \
+            (b1_y2.minimum(b2_y2) - b1_y1.maximum(b2_y1)).clamp_(0)
 
     # Union Area
     union = w1 * h1 + w2 * h2 - inter + eps
@@ -143,7 +143,7 @@ def mask_iou(mask1, mask2, eps=1e-7):
     Returns:
         (torch.Tensor): A tensor of shape (N, M) representing masks IoU.
     """
-    intersection = torch.matmul(mask1, mask2.t()).clamp(0)
+    intersection = torch.matmul(mask1, mask2.T).clamp_(0)
     union = (mask1.sum(1)[:, None] + mask2.sum(1)[None]) - intersection  # (area1 + area2) - intersection
     return intersection / (union + eps)
 

ultralytics/yolo/utils/plotting.py

@@ -10,6 +10,7 @@ import numpy as np
 import torch
 from PIL import Image, ImageDraw, ImageFont
 from PIL import __version__ as pil_version
+from scipy.ndimage.filters import gaussian_filter1d
 
 from ultralytics.yolo.utils import LOGGER, TryExcept, plt_settings, threaded
 
@@ -455,7 +456,8 @@ def plot_results(file='path/to/results.csv', dir='', segment=False, pose=False,
             for i, j in enumerate(index):
                 y = data.values[:, j].astype('float')
                 # y[y == 0] = np.nan  # don't show zero values
-                ax[i].plot(x, y, marker='.', label=f.stem, linewidth=2, markersize=8)
+                ax[i].plot(x, y, marker='.', label=f.stem, linewidth=2, markersize=8)  # actual results
+                ax[i].plot(x, gaussian_filter1d(y, sigma=3), ':', label='smooth', linewidth=2)  # smoothing line
                 ax[i].set_title(s[j], fontsize=12)
                 # if j in [8, 9, 10]:  # share train and val loss y axes
                 #     ax[i].get_shared_y_axes().join(ax[i], ax[i - 5])

ultralytics/yolo/utils/tal.py

@@ -41,7 +41,7 @@ def select_highest_overlaps(mask_pos, overlaps, n_max_boxes):
     # (b, n_max_boxes, h*w) -> (b, h*w)
     fg_mask = mask_pos.sum(-2)
     if fg_mask.max() > 1:  # one anchor is assigned to multiple gt_bboxes
-        mask_multi_gts = (fg_mask.unsqueeze(1) > 1).repeat([1, n_max_boxes, 1])  # (b, n_max_boxes, h*w)
+        mask_multi_gts = (fg_mask.unsqueeze(1) > 1).expand(-1, n_max_boxes, -1)  # (b, n_max_boxes, h*w)
         max_overlaps_idx = overlaps.argmax(1)  # (b, h*w)
 
         is_max_overlaps = torch.zeros(mask_pos.shape, dtype=mask_pos.dtype, device=mask_pos.device)
@@ -132,7 +132,7 @@ class TaskAlignedAssigner(nn.Module):
         # Get anchor_align metric, (b, max_num_obj, h*w)
         align_metric, overlaps = self.get_box_metrics(pd_scores, pd_bboxes, gt_labels, gt_bboxes, mask_in_gts * mask_gt)
         # Get topk_metric mask, (b, max_num_obj, h*w)
-        mask_topk = self.select_topk_candidates(align_metric, topk_mask=mask_gt.repeat([1, 1, self.topk]).bool())
+        mask_topk = self.select_topk_candidates(align_metric, topk_mask=mask_gt.expand(-1, -1, self.topk).bool())
         # Merge all mask to a final mask, (b, max_num_obj, h*w)
         mask_pos = mask_topk * mask_in_gts * mask_gt
 
@@ -146,15 +146,15 @@ class TaskAlignedAssigner(nn.Module):
         bbox_scores = torch.zeros([self.bs, self.n_max_boxes, na], dtype=pd_scores.dtype, device=pd_scores.device)
 
         ind = torch.zeros([2, self.bs, self.n_max_boxes], dtype=torch.long)  # 2, b, max_num_obj
-        ind[0] = torch.arange(end=self.bs).view(-1, 1).repeat(1, self.n_max_boxes)  # b, max_num_obj
-        ind[1] = gt_labels.long().squeeze(-1)  # b, max_num_obj
+        ind[0] = torch.arange(end=self.bs).view(-1, 1).expand(-1, self.n_max_boxes)  # b, max_num_obj
+        ind[1] = gt_labels.squeeze(-1)  # b, max_num_obj
         # Get the scores of each grid for each gt cls
         bbox_scores[mask_gt] = pd_scores[ind[0], :, ind[1]][mask_gt]  # b, max_num_obj, h*w
 
         # (b, max_num_obj, 1, 4), (b, 1, h*w, 4)
-        pd_boxes = pd_bboxes.unsqueeze(1).repeat(1, self.n_max_boxes, 1, 1)[mask_gt]
-        gt_boxes = gt_bboxes.unsqueeze(2).repeat(1, 1, na, 1)[mask_gt]
-        overlaps[mask_gt] = bbox_iou(gt_boxes, pd_boxes, xywh=False, CIoU=True).squeeze(-1).clamp(0)
+        pd_boxes = pd_bboxes.unsqueeze(1).expand(-1, self.n_max_boxes, -1, -1)[mask_gt]
+        gt_boxes = gt_bboxes.unsqueeze(2).expand(-1, -1, na, -1)[mask_gt]
+        overlaps[mask_gt] = bbox_iou(gt_boxes, pd_boxes, xywh=False, CIoU=True).squeeze(-1).clamp_(0)
 
         align_metric = bbox_scores.pow(self.alpha) * overlaps.pow(self.beta)
         return align_metric, overlaps
@@ -273,4 +273,4 @@ def dist2bbox(distance, anchor_points, xywh=True, dim=-1):
 def bbox2dist(anchor_points, bbox, reg_max):
     """Transform bbox(xyxy) to dist(ltrb)."""
     x1y1, x2y2 = bbox.chunk(2, -1)
-    return torch.cat((anchor_points - x1y1, x2y2 - anchor_points), -1).clamp(0, reg_max - 0.01)  # dist (lt, rb)
+    return torch.cat((anchor_points - x1y1, x2y2 - anchor_points), -1).clamp_(0, reg_max - 0.01)  # dist (lt, rb)

ultralytics/yolo/utils/torch_utils.py

@@ -205,6 +205,20 @@ def get_flops(model, imgsz=640):
         return 0
 
 
+def get_flops_with_torch_profiler(model, imgsz=640):
+    # Compute model FLOPs (thop alternative)
+    model = de_parallel(model)
+    p = next(model.parameters())
+    stride = (max(int(model.stride.max()), 32) if hasattr(model, 'stride') else 32) * 2  # max stride
+    im = torch.zeros((1, p.shape[1], stride, stride), device=p.device)  # input image in BCHW format
+    with torch.profiler.profile(with_flops=True) as prof:
+        model(im)
+    flops = sum(x.flops for x in prof.key_averages()) / 1E9
+    imgsz = imgsz if isinstance(imgsz, list) else [imgsz, imgsz]  # expand if int/float
+    flops = flops * imgsz[0] / stride * imgsz[1] / stride  # 640x640 GFLOPs
+    return flops
+
+
 def initialize_weights(model):
     """Initialize model weights to random values."""
     for m in model.modules():

ultralytics/yolo/v8/classify/val.py

@@ -60,8 +60,7 @@ class ClassificationValidator(BaseValidator):
         return self.metrics.results_dict
 
     def build_dataset(self, img_path):
-        dataset = ClassificationDataset(root=img_path, args=self.args, augment=False)
-        return dataset
+        return ClassificationDataset(root=img_path, args=self.args, augment=False)
 
     def get_dataloader(self, dataset_path, batch_size):
         """Builds and returns a data loader for classification tasks with given parameters."""